Conumee 2.0: enhanced copy-number variation analysis from DNA methylation arrays for humans and mice.

MOTIVATION: Copy-number variations (CNVs) are common genetic alterations in cancer and their detection may impact tumor classification and therapeutic decisions. However, detection of clinically relevant large and focal CNVs remains challenging when sample material or resources are limited. This has motivated us to create a software tool to infer CNVs from DNA methylation arrays which are often generated as part of clinical routines and in research settings. RESULTS: We present our R package, conumee 2.0, that combines tangent normalization, an adjustable genomic binning heuristic, and weighted circular binary segmentation to utilize DNA methylation arrays for CNV analysis and mitigate technical biases and batch effects. Segmentation results were validated in a lung squamous cell carcinoma dataset from TCGA (n = 367 samples) by comparison to segmentations derived from genotyping arrays (Pearson's correlation coefficient of 0.91). We further introduce a segmented block bootstrapping approach to detect focal alternations that achieved 60.9% sensitivity and 98.6% specificity for deletions affecting CDKN2A/B (60.0% and 96.9% for RB1, respectively) in a low-grade glioma cohort from TCGA (n = 239 samples). Finally, our tool provides functionality to detect and summarize CNVs across large sample cohorts. AVAILABILITY AND IMPLEMENTATION: Conumee 2.0 is available under open-source license at: https://github.com/hovestadtlab/conumee2.

Determination of confocal profile and curved focal plane for OCT mapping of the attenuation coefficient.

The attenuation coefficient has proven to be a useful tool in numerous biological applications, but accurate calculation is dependent on the characterization of the confocal effect. This study presents a method to precisely determine the confocal effect and its focal plane within a sample by examining the ratio of two optical coherence tomography (OCT) images. The method can be employed to produce a single-value estimate, or a 2D map of the focal plane accounting for the curvature or tilt within the sample. Furthermore, this method is applicable to data obtained with both high numerical aperture (NA) and low-NA lenses, thereby furthering the applicability of the attenuation coefficient to high-NA OCT data. We test and validate this method using standard samples of Intralipid 20% and 5%, improving the accuracy to 99% from 65% compared to the traditional method and preliminarily show applicability to real biological data of glioblastoma acquired in vivo in a murine model.